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AI Recurring Payment Failure Predictor

Cole Mueller

Full-time · Job · Chicago

About the role

A subscription business that runs a monthly charge and gets declined has a problem it can handle in several ways: retry immediately, retry at a different time of day, retry with a different retry interval, pre-emptively reach out to the customer, or update the payment method. Which approach works best depends on why the charge failed — insufficient funds, expired card, bank-side fraud hold, or network error — and predicting that reason before the retry is what smart dunning management looks like. We process recurring payments for 85 subscription businesses with combined annual recurring revenue of $4B. Payment failure prediction is the ML capability that makes our dunning management intelligent. We can predict payment failure probability 3-5 days before a charge attempt with 78% precision. We can't yet predict failure reason — which is what drives the dunning strategy selection. You'll build the failure reason prediction system: engineering features from prior payment history, bank response codes, account behavior, and macro signals (end-of-month balance pressure), training multi-class failure reason models, and integrating predictions into the dunning strategy selection logic that decides retry timing, channel, and intervention type for each subscriber.

Responsibilities

  • Build multi-class payment failure reason prediction: insufficient funds, expired card, fraud hold, network error
  • Engineer features from payment history, bank response patterns, subscriber behavior, and calendar signals
  • Integrate failure reason predictions into dunning strategy selection for retry timing and channel decisions
  • Develop prediction calibration ensuring probability estimates are reliable for strategy thresholds
  • Create monitoring tracking failure reason prediction accuracy against realized bank response codes
  • Build failure reason trend analysis identifying systematic shifts in decline composition

Requirements

  • 3+ years in data science or ML with payments, subscription analytics, or financial services experience
  • Strong Python, SQL, and scikit-learn skills for structured data classification
  • Understanding of payment processing, bank response codes, and subscription billing dynamics
  • Experience building classification systems with calibrated probability outputs
  • Ability to integrate ML predictions into operational workflow decision logic

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $3,000 annual professional development budget
  • 401(k) with match
  • Performance bonus tied to dunning recovery improvement

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Categories

data science & analytics AI Business, Marketing & Strategy

Skills

python scikit-learn sql pandas statistics

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Level

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Language

English

Salary Range

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AI Expertise

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Skills

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